scholarly journals PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pablo Acera Mateos ◽  
Renzo F. Balboa ◽  
Simon Easteal ◽  
Eduardo Eyras ◽  
Hardip R. Patel
Keyword(s):  
Deep Learning ◽  
Viral Infections ◽  
Learning Algorithm ◽  
Rna Viruses ◽  
Respiratory Viruses ◽  
Rna Seq ◽  
Deep Learning Algorithm ◽  
Learning Classifier ◽  
Global Pandemic

AbstractViral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning algorithm that accurately detects SARS-CoV-2 and other common RNA respiratory viruses from RNA-seq data. Using in silico data, PACIFIC recovers the presence and relative concentrations of viruses with > 99% precision and recall. PACIFIC accurately detects SARS-CoV-2 and other viral infections in 63 independent in vitro cell culture and patient datasets. PACIFIC is an end-to-end tool that enables the systematic monitoring of viral infections in the current global pandemic.

scholarly journals PACIFIC: A lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses

2020 ◽  
Author(s):  
Pablo Acera Mateos ◽  
Renzo F. Balboa ◽  
Simon Easteal ◽  
Eduardo Eyras ◽  
Hardip R. Patel
Keyword(s):  
Deep Learning ◽  
Viral Infections ◽  
Learning Algorithm ◽  
Rna Viruses ◽  
Respiratory Viruses ◽  
Rna Seq ◽  
Deep Learning Algorithm ◽  
Learning Classifier ◽  
Global Pandemic

AbstractViral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning algorithm that accurately detects SARS-CoV-2 and other common RNA respiratory viruses from RNA-seq data. Using in silico data, PACIFIC recovers the presence and relative concentrations of viruses with >99% precision and recall. PACIFIC accurately detects SARS-CoV-2 and other viral infections in 63 independent in vitro cell culture and patient datasets. PACIFIC is an end-to-end tool that enables the systematic monitoring of viral infections in the current global pandemic.

scholarly journals Transfer Learning via Artificial Intelligence for Guiding Implant Placement in the Posterior Mandible: an in vitro Study

2021 ◽  
Author(s):  
Yun Liu ◽  
Zhi-cong Chen ◽  
Chun-ho Chu ◽  
Fei-Long Deng
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
In Vitro Study ◽  
Angular Deviation ◽  
Single Shot ◽  
Implant Position ◽  
Deep Learning Algorithm ◽  
Group A

Abstract Background: To explore the capacity of a single shot multibox detector (SSD) and Voxel-to-voxel prediction network for pose estimation (V2V-PoseNet) based artificial intelligence (AI) system in automatically designing implant plan. Methods: 2500 and 67 cases were used to develop and pre-train the AI system. After that, 12 patients who missed the mandibular left first molars were selected to test the capacity of the AI in automatically designing implant plan. There were three algorithms-based implant positions. They are Group A, B and C (8, 9 and 10 points dependent implant position, respectively). The AI system was then used to detect the characteristic annotators and determine the implant position. For every group, the actual implant position was compared with the algorithm-determined ideal position. And global, angular, depth and lateral deviation were calculate. One-way ANOVA followed by Tukey’s test was performed for statistical comparisons. The significance value was set at P< 0.05. Results: Group C represented the least coronal (0.6638±0.2651, range: 0.2060 to 1.109 mm) and apical (1.157±0.3350, range: 0.5840 to 1.654 mm) deviation, the same trend was observed in the angular deviation (5.307 ±2.891°, range: 2.049 to 10.90°), and the results are similar with the traditional statistic guide.Conclusion: It can be concluded that the AI system has the capacity of deep learning. And as more characteristic annotators be involved in the algorithm, the AI system can figure out the anatomy of the object region better, then generate the ideal implant plan via deep learning algorithm.

scholarly journals Using deep learning algorithm to surmount drawbacks of statistical method in the procession of identify related genes with cancer

2019 ◽  
Author(s):  
Bohao Zou
Keyword(s):  
Deep Learning ◽  
Tumor Cell ◽  
Statistical Method ◽  
Information Gain ◽  
Learning Algorithm ◽  
Statistical Test ◽  
Rna Seq ◽  
Exon Expression ◽  
Deep Learning Algorithm ◽  
Key Genes

AbstractFinding key genes which are relative with cancer is the first essential step to understand what has taken place in the tumor cell. At present, the most methods which can discover key genes make a contrast between normal samples and tumor samples and base on the statistical test. However, those methods face on some problems like the insufficient of statistical test in unbalanced samples, defect of only using single data that can not display the holistic situation in tumor cell. For solving those issues, i proposed a innovation method that uses semi-supervised and unsupervised algorithm to discover key genes which are linked with cancer. The genes in the final result list are not only in the double category but with distinct hierarchy and those genes are all detected from diversity data like methylation, gene expression RNA-Seq, exon expression RNA-Seq and so on. At last, for comparing the result of this method and traditional statistical method, i used the conception of information gain ratio to prove the advantage of this deep learning method in mathematical.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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